Impairments in cognitive ability and attention are pervasive and potentially debilitating components of many disorders, conditions, injuries and diseases, including mild cognitive impairment (MCI) in persons with pre-dementia, dementia, dementia with Lewy bodies, Alzheimer's Disease, traumatic brain injury, Attention Deficit/Hyperactivity Disorder (ADHD), and cognitive/attentional declines associated with chronic diseases such as diabetes, cardiovascular disease, and HIV infection [1, 2, 3, 4, 5, 6, 7, 8]. Most of these disorders are assumed to be pathology-based and therefore amenable to intervention, especially if diagnosed early. Despite the staggering number of such conditions, the significance of such cognitive and attentional impairments in these conditions, and the importance of early, accurate, and comprehensive assessment and diagnosis, there is currently no such procedure or set of standards to employ to quantify such impairments, either when diagnosing the disorder or examining effectiveness of treatment.
For example, the recent NIH Consensus Statement on Attention Deficit/Hyperactivity Disorder [9] concluded that ADHD is difficult to diagnose, considered a common problem, and is associated with many negative consequences, both for the patient and society, and has been inconsistently associated with neuroimaging and EEG anomalies that have been non-diagnostic in nature.
ADHD is one of multiple disorders associated with impairments in attention. Although this document may particularly identify attentional disorders associated with ADHD, the present invention shall be applied to any disorder with associated attentional impairments. With respect to dementia, recent research and a review of the literature conclude that the frequency of post stroke dementia and cognitive decline varied sharply when different systems of diagnostic classification and methods were used [10]. Furthermore, recent findings support the need for validation not only of the criteria, but also the need for validated measures to diagnosis dementia and cognitive impairment post stroke [10, 11, 12], and Alzheimer's disease [13]. In addition, cognitive abnormalities commonly occur in patients with HIV infection [14]. Among otherwise healthy HIV-positive patients, cognitive deficits are thought to be infrequent [15], but some investigators suggest that more sensitive measures may be needed to detect the mild cognitive decline during the asymptomatic stage [16].
Diagnostic Dilemma
There are numerous disorders and diseases associated with impairment of attention and cognitive functioning, however, the diagnosis and quantification of impairment of attention in any disease or disorder is typically difficult. Some examples include: attentional impairments associated with ADHD, HIV infection, Alzheimer's Disease, cardiovascular disease, diabetes, and dementia.
With respect to ADHD, the DSM-IV [17] states “The essential features of ADHD is a persistent pattern of inattention and/or hyperactivity-impulsivity that is more frequent and severe than is typically observed in individuals in a comparable level of development.” Evidence of six of nine inattentive behaviors and/or six of nine hyperactive-impulsive behaviors must have been present before age seven, and must clearly interfere with social, academic and/or occupational functioning. Consequently, the diagnosis of ADHD is highly dependent on a retrospective report of a patient's past behavior and subjective judgements on degree of relative impairment. Due to the subjective nature of assessment, precision in diagnosis has been elusive. ADHD is complex and influences all aspects of a person's life. It can co-exist with and/or mimic a variety of health, emotional, learning, cognitive, and language problems. An appropriate, comprehensive evaluation for ADHD includes a medical, educational, and behavioral history, evidence of normal vision and hearing, recognition of systemic illness, and a developmental survey. The diagnosis of ADHD should never be made based exclusively on rating scales, questionnaires, or tests [18].
Prevalence
Since ADHD cannot be strictly defined, and precisely and objectively measured, its true prevalence cannot be accurately determined. While the DSM-IV estimates the prevalence of ADHD in school-age children as between three percent and five percent, other community survey studies suggest it may be as high as 16 percent [19]. ADHD occurs more commonly in males than in females, with ratios ranging from 4:1 to 9:1. Of all child referrals for mental health services, one-third to one-half is thought to be attributable to ADHD.
According to recent projections [20], Alzheimer's disease will affect increasing numbers of people as baby boomers (persons born between 1946 and 1964) age. The annual number of incident cases is expected to more than double by the midpoint of the twenty-first century: from 377,000 in 1995 to 959,000 in 2050. The proportion of new onset cases that are age 85 or older will increase from forty percent in 1995 to 62 percent in 2050.
It is clear from the number of persons suffering from attentional or cognitive difficulties or deficits, that there is a need for accurate diagnosis and validation of treatment efficacy. It is also clear that the portion of the population who will be suffering from cognitive decline or impairment will continue to increase with the overall aging of the population and the increased diagnosis of attentional disorders. There is therefore a need in the art for a comprehensive, flexible, and an effective diagnostic measure of attentional abilities.
Negative Consequences
The hallmarks of ADHD are hyperactivity, impulsivity, and an inability to sustain attention. The DSM-IV distinguishes three types: predominantly inattentive type, predominantly hyperactive-impulsive type, and combined type. In addition to the core clinical symptoms of ADHD, high levels of co-morbidity have been found with learning, oppositional defiant, conduct, mood, and anxiety disorders. Furthermore, it is estimated that the majority of children diagnosed with ADHD exhibit significant behavioral problems during adolescence and manifest continuing functional deficits and psychopathology into adulthood. One real-life consequence of ADHD is a five-fold increase in automobile crashes [21].
Early diagnosis and treatment of Alzheimer's disease, dementia, and additional progressive disorders associated with attentional impairment is especially important because patients with early stages of dementia may show reversal of their cognitive deficits and neurochemistry abnormalities after treatment [8].
Neuroimaging and EEG Findings Related to ADHD
In spite of these well-documented problems, the mechanisms and etiology of ADHD remain methodologically difficult to study, with different studies yielding inconsistent results. Most investigators accept that ADHD exists as a distinct clinical syndrome and suggest a multifactorial etiology that includes neurobiology as an important factor. Zametkin and Rapoport [22] identified eleven separate neuroanatomical hypotheses that have been proposed for the etiology of ADHD. Most studies have concluded that either delayed maturation or defects in cortical activation play large roles in the pathophysiology of ADHD. For example, studies of cerebral blood flow determined by single-positron emission computerized tomography have demonstrated decreased metabolic activity in suspected attentional areas of the brain [23]. These, as well as additional neurophysiological findings, have been interpreted as evidence of delayed maturation and cortical hypoarousal in regions of the prefrontal and frontal cortex, the two predominate etiological theories underlying ADHD. Unfortunately, while neuroanatomical findings lend support to the notion that ADHD is a distinct clinical syndrome and add to our understanding of the etiology of ADHD, neuroimaging techniques are too expensive, restricted to a few centers, and lack clear specificity and sensitivity in diagnosis of ADHD. There is therefore a need in the art for an inexpensive and clear system and method for diagnosis of ADHS and other impairments.
There are a few basic methods of analyzing EEG data that have been employed in previous research—visual inspection of raw data and quantitative analyses of EEG data, including spectral and coherence methods of analysis. To date, none of these methods have revealed pervasive or consistent patterns of EEG abnormalities with sufficient specificity or sensitivity to separate children with ADHD from normal subjects. The first of these methods of analysis involves visual inspection of raw EEG data. As long ago as 1938, Jasper, Solomon, and Bradley, used this method and reported EEG abnormalities in children with minimal brain dysfunction (an outdated term used to describe children with hyperactivity and poor attentiveness as well as learning disabilities and conduct disorder). In parallel with the development of the computer, researchers have applied a second method of EEG analysis employing quantitative techniques. Quantitative EEG is a mathematical analysis of voltage-time series data with the intention of extracting useful information not readily apparent to visual inspection. Spectral analysis is a common technique of quantitative EEG. It mathematically transforms, via Fast Fourier Transform (FFT), raw amplitude-time data into its component frequencies. During the 1970s several laboratories utilized a combination of visual inspection and quantitative techniques, and reported differences between the EEGs of hyperactive and normal children. Among the differences discovered were: a higher percentage of abnormal EEG patterns (abnormal usually meaning excessive slow wave activity) in clinical subjects than in controls; more power in the 0 to 8 Hz spectrum in hyperactive children compared to normal controls; less power in the 10 Hz range for hyperactives than controls; and less beta and weaker stimulus-locked alpha attenuation in hyperactive than in non-hyperactive children. These early studies were typically confounded by inconsistent and often inadequate assessment procedures and methodologies. It is therefore not surprising that early research demonstrated no pervasive or consistent patterns of EEG data to discriminate hyperactive, inattentive, or impulsive children from controls.
Noticeably absent in the literature of that time, however, was information concerning extensive EEG frequency components obtained from several groups of clinical and control children engaged in tasks manipulating attention. Numerous investigators have reported that only when subjects are engaged in behavioral paradigms (particularly those manipulating attention) do electrophysiological differences appear between normal and hyperactive or LD children. Partially in response to this deficit in the research literature, Dykman et al. [24] investigated the EEGs of four groups of boys (10 hyperactive, 10 learning-disabled, 10 with both hyperactivity and LD) engaged in a complex visual search task. Spectral analysis of EEG data indicated that LD boys, hyperactive boys, and boys with a mixed diagnosis displayed less beta and less stimulus-locked alpha attenuation than normal boys. Thus, research in the 1980s-1990s began to address and correct issues of uniformity of diagnosis, methodology, and accuracy in EEG acquisition, both in terms of theoretical understanding and technical application. In an attempt to clarify some of the EEG differences between hyperactive and normal subjects, Satterfield, Schell, Backs & Hidaka [25] considered the impact of age upon EEG in two groups of normal (n=60) and hyperactive, inattentive, and impulsive males (n=138) ages 6-12, by examining follow-up EEGs on a subset of the hyperactive and normal subjects four years after the initial EEG. Their findings indicate that EEG power spectral intensities of normal male children decrease with increasing age. However, EEG power declines slower with increasing age in hyperactive subjects. Overall, instead of clarifying the issues, Satterfield et al. conclude that “. . . electrophysiological differences between hyperactive and normal male children are complex and vary markedly with age.” They further warn that “Computation of group averages which include data from children of a wide age range may obscure rather than clarify the electrophysiological correlates of this disorder.”
More recent studies employing spectral analysis of EEG have also shown varying patterns of EEG activity in ADHD subjects. Mann, et al. [26] tested 25 nine to twelve year-old boys with predominantly inattentive-type ADHD, and found increased theta at both absolute and relative percent power calculations, and decreased beta in temporal and frontal sites. Janzen et al. [27] compared EEG differences between eight ADD males and eight normal control males ages 9-12. Results demonstrated that the ADD males had higher theta amplitudes for all sites. However, unlike Mann et al., Janzen et al. found no differences between groups for beta-all amplitudes. Clarke et al. [28] performed automated EEG on subjects (ages 8-12) classified into groups of 20 ADHD-Combined Type, 20 ADHD-Predominantly Inattentive Type, and 20 controls. Overall, they found evidence of increased absolute and relative theta in all ADHD subjects, with the ADHD combined type showing a significantly greater amount of theta power than the predominantly inattentive-type. In addition, Clarke et al. found a decrease in alpha activity, but elevated theta present in all brain regions measured and not confined to frontal regions as previous studies had reported. In contrast to Mann, et al. they report less posterior absolute beta power in posterior regions. In an interesting study by Ackerman, et al. [29] a group of 56 ADD/ADHD children who had normal reading skills were employed as a control group, and their EEGs compared to EEGs of 119 children with reading disorders (some of whom had a co-morbid diagnosis of ADD/ADHD). Subjects included 86 males and 33 females between the ages of 7.5 and 12 years. Coherence analysis of EEG data is an additional method of quantitative analysis employed in a smaller number of studies, with equally inconclusive findings. Coherence analysis involves a cross-correlation that measures the relationship of activity at one site of the brain to another. In one of the largest studies procured to date, Chabot and Serfontein [30] tested 407 children with attention deficits with and without hyperactivity, with and without learning problems, children with attention problems who failed to reach DSM-III criteria for the disorder, and 310 controls (ages 6-17). They first employed spectral analysis and observed patterns of excess theta in frontal regions and increased alpha (relative power) in the posterior regions for the clinical groups versus controls. They then employed coherence analysis and reported that one-third of the non-control children showed signs of interhemispheric dysregulation characterized by this pattern of excessive theta/alpha power in the right temporal and premotor (frontal) areas.
Overall, although numerous studies have examined ADHD versus non-ADHD children using EEG, techniques in study design vary widely. Of the studies above, sixty percent involve only male subjects, eight of eleven studies used electrode caps for EEG acquisition, and only three employed a clinical control group in addition to a normal control group. Only seven of the studies specifically evaluate EEGs of diagnosed ADHD children (versus children displaying attentional deficits and no hyperactivity). Of these studies, five did report increased theta wave activity. However, these findings were not consistently found to involve similar brain regions (two in frontal region, one parietal region, one anterior region, and one all sites). Two of the seven studies reported decreased alpha wave activity, while two reported increased alpha relative power, and the remaining three reported no significant alpha wave findings. Again, of the seven studies involving ADHD diagnosed subjects, one reported decreased absolute beta in the posterior regions, one reported decreased relative beta in the posterior regions, one reported decreased beta in the right frontal region, two reported increased beta wave activity, and two reported no significant beta findings. The presence of theta and the absence of beta may be the neural substrate of the inability to shift between tasks in order to focus on the task at hand. This is affirmed in recent papers that hypothesize that an ADHD individual has difficulty in responding to the target task, not difficulty with ignoring peripheral stimuli [31]. Overall, the differences in EEG spectra between affected and unaffected children remain inconsistent and nonspecific enough to prevent their use as a diagnostic tool. In fact, in their 1993 review, Goldstein and Ingersoll [38] concluded that consistent differences in EEG have not been documented between those with and without ADHD.
There is therefore a need in the art for a method and apparatus for assessing attentional impairments of persons. The present invention provides a method for evaluating and quantifying comprehensive data from persons with attentional disorders. This data includes EEG information when transitioning from one cognitive task to another, behavioral information, cognitive performance, and history of symptoms. The data is examined within a sequential stochastic procedure, and used to diagnosis attentional disorders and evaluate treatment response.